In an aging population, elderly patient safety is a primary concern at hospitals and nursing homes, which demands for increased nurse care. By performing nurse activity recognition, we can not only make sure that all patients get an equal desired care, but it can also free nurses from manual documentation of activities they perform, leading to a fair and safe place of care for the elderly. In this work, we present a multimodal transformer-based network, which extracts features from skeletal joints and acceleration data, and fuses them to perform nurse activity recognition. Our method achieves state-of-the-art performance of 81.8% accuracy on the benchmark dataset available for nurse activity recognition from the Nurse Care Activity Recognition Challenge. We perform ablation studies to show that our fusion model is better than single modality transformer variants (using only acceleration or skeleton joints data). Our solution also outperforms state-of-the-art ST-GCN, GRU and other classical hand-crafted-feature-based classifier solutions by a margin of 1.6%, on the NCRC dataset. Code is available at \url{https://github.com/Momilijaz96/MMT_for_NCRC}.
Singapore University of Technology and Design（新加坡科技设计大学）；Institute for Infocomm Research（新加坡资讯通信研究院）
With the increasing availability of wearable devices, research on egocentric activity recognition has received much attention recently. In this paper, we build a Multimodal Egocentric Activity dataset which includes egocentric videos and sensor data of 20 fine-grained and diverse activity categories. We present a novel strategy to extract temporal trajectory-like features from sensor data. We propose to apply the Fisher Kernel framework to fuse video and temporal enhanced sensor features. Experiment results show that with careful design of feature extraction and fusion algorithm, sensor data c…
We introduce a system that recognizes concurrent activities from real-world data captured by multiple sensors of different types. The recognition is achieved in two steps. First, we extract spatial and temporal features from the multimodal data. We feed each datatype into a convolutional neural network that extracts spatial features, followed by a long-short term memory network that extracts temporal information in the sensory data. The extracted features are then fused for decision making in the second step. Second, we achieve concurrent activity recognition with a single classifier that enco…
GPT-4o: Visual perception performance of multimodal large language models in piglet activity understanding
China Agricultural University（中国农业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；AniEye Technology（安尼眼科技）；State Key Laboratory of Animal Nutrition and Feeding（动物营养与饲料国家重点实验室）
To fluently collaborate with people, robots need the ability to recognize human activities accurately. Although modern robots are equipped with various sensors, robust human activity recognition (HAR) still remains a challenging task for robots due to difficulties related to multimodal data fusion. To address these challenges, in this work, we introduce a deep neural network-based multimodal HAR algorithm, HAMLET. HAMLET incorporates a hierarchical architecture, where the lower layer encodes spatio-temporal features from unimodal data by adopting a multi-head self-attention mechanism. We devel…
The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for passive Wi-Fi sensing are based on passive Wi-Fi radar (PWR) and channel state information (CSI) data, however current systems do not effectively exploit the information acquired through multiple sensors to recognise the different activities. In this paper, we explore new properties of the Transformer architecture for multimodal sensor fusion. We study different signal processing techniques to extract multiple image-ba…
Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics
Centre for Learning Analytics at Monash（墨尔本大学学习分析中心）；Monash University（墨尔本大学）；Department of Civil and Environmental Engineering（土木与环境工程系）；School of Education（教育学院）；The University of Hong Kong（香港大学）
Human Activity Recognition is a field of research where input data can take many forms. Each of the possible input modalities describes human behaviour in a different way, and each has its own strengths and weaknesses. We explore the hypothesis that leveraging multiple modalities can lead to better recognition. Since manual annotation of input data is expensive and time-consuming, the emphasis is made on self-supervised methods which can learn useful feature representations without any ground truth labels. We extend a number of recent contrastive self-supervised approaches for the task of Huma…
Nanjing Normal University（南京师范大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Yunnan University（云南大学）
Human activity recognition (HAR) in ubiquitous computing has been beginning to incorporate attention into the context of deep neural networks (DNNs), in which the rich sensing data from multimodal sensors such as accelerometer and gyroscope is used to infer human activities. Recently, two attention methods are proposed via combining with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) network, which can capture the dependencies of sensing signals in both spatial and temporal domains simultaneously. However, recurrent networks often have a weak feature representing power compared …
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition
School of Electrical and Electronics Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航天工程学院）
We present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. We first compare the effect of placing various constraints on the embedding space using paired text and video data. We also propose a method to improve the joint embedding space using an adversarial formulation, allowing it to benefit from unpaired text and video data. By using unpaired text data, we show the ability to learn a representation that better captures unseen activities. In addition to testing on publicly available datasets, we introduce a new, large-scale te…
We present the Multiview Extended Video with Activities (MEVA) dataset, a new and very-large-scale dataset for human activity recognition. Existing security datasets either focus on activity counts by aggregating public video disseminated due to its content, which typically excludes same-scene background video, or they achieve persistence by observing public areas and thus cannot control for activity content. Our dataset is over 9300 hours of untrimmed, continuous video, scripted to include diverse, simultaneous activities, along with spontaneous background activity. We have annotated 144 hour…
A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System
Academy of Scientific and Innovative Research (AcSIR)（科学与创新研究院）；CSIR-Central Electronics Engineering Research Institute (CEERI)（CSIR-中央电子工程研究所）；Birla Institute of Technology and Science Pilani（毕拉理工学院皮拉尼分校）；Edge Hill University（边山大学）
University of Catania（卡塔尼亚大学）；University of Central Florida（中佛罗里达大学）
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that max…
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Considerable attention has been paid for physiological signal-based emotion recognition in field of affective computing. For the reliability and user friendly acquisition, Electrodermal Activity (EDA) has great advantage in practical applications. However, the EDA-based emotion recognition with hundreds of subjects still lacks effective solution. In this paper, our work makes an attempt to fuse the subject individual EDA features and the external evoked music features. And we propose an end-to-end multimodal framework, the 1-dimensional residual temporal and channel attention network (RTCAN-1D…
A Large-Scale Multimodal Dataset and Benchmarks for Human Activity Scene Understanding and Reasoning
The Chinese University of Hong Kong, Hong Kong（香港中文大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）；University of Pittsburgh（匹兹堡大学）
We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture. Our focus was to find an approach that generalizes well across different sensor modalities without specific adaptions while still achieving good results. We apply our method to 4 action recognition datasets containing skeleton sequences, inertial and motion capturing measurements as well as \wifi fingerprints that range up to 120 action classes. …
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations
Idiap Research Institute（伊迪亚普研究所）；University of Perugia（佩鲁贾大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Queen Mary University of London（伦敦玛丽女王大学）
MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；Arexeni Research and Technologies Inc.（Arexeni研究与技术公司）；Beijing Sports University（北京体育大学）；University of Michigan（密歇根大学）
MHAD: Multimodal Home Activity Dataset with Multi-Angle Videos and Synchronized Physiological Signals
JD Health, Beijing, China（京东健康，北京，中国）；School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China（华中科技大学软件工程学院，武汉，中国）；College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China（浙江大学生物医学工程与仪器科学学院，杭州，中国）
Human activity recognition (HAR) based on multimodal sensors has become a rapidly growing branch of biometric recognition and artificial intelligence. However, how to fully mine multimodal time series data and effectively learn accurate behavioral features has always been a hot topic in this field. Practical applications also require a well-generalized framework that can quickly process a variety of raw sensor data and learn better feature representations. This paper proposes a universal multi-sensor network (UMSNet) for human activity recognition. In particular, we propose a new lightweight s…
Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework. These four types of activity images are Signal Images (SI), Gramian Angular Field (GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP) Images. Furthermore, for creating a multimodal fusion framework …
Multi-modal systems are quite common in the context of human activity recognition; widely used RGB-D sensors (Kinect is the most prominent example) give access to parallel data streams, typically RGB images, depth data, skeleton information. The richness of multimodal information has been largely exploited in many works in the literature, while an analysis of their effectiveness for incremental template updating has not been investigated so far. This paper is aimed at defining a general framework for unsupervised template updating in multi-modal systems, where the different data sources can pr…
Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model
SenSIP Center, School of ECEE, Arizona State University（SenSIP中心，电子与计算机工程学院，亚利桑那州立大学）；Prime Solutions Group Inc（Prime Solutions Group公司）；Intelligence Advanced Research Projects Activity（智能高级研究计划局）
Lomonosov Moscow State University（莫斯科国立大学）；Huawei Moscow Research Center（华为莫斯科研究中心）
We present a new state-of-the-art on the text to video retrieval task on MSRVTT and LSMDC benchmarks where our model outperforms all previous solutions by a large margin. Moreover, state-of-the-art results are achieved with a single model on two datasets without finetuning. This multidomain generalisation is achieved by a proper combination of different video caption datasets. We show that training on different datasets can improve test results of each other. Additionally we check intersection between many popular datasets and found that MSRVTT has a significant overlap between the test and th…
Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-ofHearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voice-controlled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with t…
Loughborough University London（拉夫堡大学伦敦校区）；Institute for Digital Technologies（数字技术研究所）
Human Activity Recognition (HAR) is a key building block of many emerging applications such as intelligent mobility, sports analytics, ambient-assisted living and human-robot interaction. With robust HAR, systems will become more human-aware, leading towards much safer and empathetic autonomous systems. While human pose detection has made significant progress with the dawn of deep convolutional neural networks (CNNs), the state-of-the-art research has almost exclusively focused on a single sensing modality, especially video. However, in safety critical applications it is imperative to utilize …
In this paper, we examined the zero-shot activity recognition task with the usage of videos. We introduce an auto-encoder based model to construct a multimodal joint embedding space between the visual and textual manifolds. On the visual side, we used activity videos and a state-of-the-art 3D convolutional action recognition network to extract the features. On the textual side, we worked with GloVe word embeddings. The zero-shot recognition results are evaluated by top-n accuracy. Then, the manifold learning ability is measured by mean Nearest Neighbor Overlap. In the end, we provide an extens…
